{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "survival: Survival\n",
    "\n",
    "PassengerId: Unique Id of a passenger.\n",
    "\n",
    "pclass: Ticket class\n",
    "\n",
    "sex: Sex\n",
    "\n",
    "Age: Age in years\n",
    "\n",
    "sibsp: # of siblings / spouses aboard the Titanic\n",
    "\n",
    "parch: # of parents / children aboard the Titanic\n",
    "\n",
    "ticket: Ticket number\n",
    "\n",
    "fare: Passenger fare\n",
    "\n",
    "cabin: Cabin number\n",
    "\n",
    "embarked: Port of Embarkationsurvival: Survival"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入库\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "# data visualization\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "# Algorithms\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv(\"test.csv\")\n",
    "train_df = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析数据格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "简单的变量统计值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "前8条数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到有许多NaN, 需要处理；并且数据要标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cabin</th>\n",
       "      <td>687</td>\n",
       "      <td>77.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>177</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticket</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total     %\n",
       "Cabin       687  77.1\n",
       "Age         177  19.9\n",
       "Embarked      2   0.2\n",
       "Fare          0   0.0\n",
       "Ticket        0   0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = train_df.isnull().sum().sort_values(ascending=False)  # 共有多少个\n",
    "percent_1 = train_df.isnull().sum() / train_df.isnull().count() * 100  # sum按列加，所以会把所有的true（naN) 加上; count(): count no-NA cells\n",
    "percent_2 = (round(percent_1, 1)).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '%'])\n",
    "missing_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "age需要处理一下，Cabin缺的太多最好直接舍弃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PassengerId, Name, Ticket(number)应该没什么用（name可能有用呢，ticket的信息应该包含在其他列里了）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Age and Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived = 'survived'\n",
    "not_survived = 'not survived'\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 4))\n",
    "\n",
    "women = train_df[train_df['Sex'] == 'female']\n",
    "men = train_df[train_df['Sex'] == 'male']\n",
    "\n",
    "# 对于age的分布直方图，纵坐标为个数\n",
    "ax = sns.distplot(women[women['Survived'] == 1].Age.dropna(), bins=18, \n",
    "                 label=survived, ax=axes[0], kde=False)\n",
    "ax = sns.distplot(women[women['Survived']==0].Age.dropna(), bins=40, label=not_survived, ax=axes[0], kde=False)\n",
    "ax.legend()\n",
    "ax.set_title('Female')\n",
    "\n",
    "ax = sns.distplot(men[men['Survived'] == 1].Age.dropna(), bins=18, label=survived, ax=axes[1], kde=False)\n",
    "ax = sns.distplot(men[men['Survived'] == 0].Age.dropna(), bins=40, label=not_survived, ax=axes[1], kde=False)\n",
    "ax.legend()\n",
    "_ = ax.set_title('Male')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**可以看到：**\n",
    "\n",
    "1. 女性整体生存概率是高于男性的\n",
    "\n",
    "2. 女性在20岁左右生存概率最大，相反男性在20岁左右却比较低\n",
    "\n",
    "3. 男女的新生儿生存概率都较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Embarked, Pclass and Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x177288c9128>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 588.9x972 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 横坐标是Pclass 纵坐标是生存率 蓝色女性 黑色男性 每一幅图分别是不同的Embarkment port\n",
    "FacetGrid = sns.FacetGrid(train_df, row='Embarked', size=4.5, aspect=1.6)\n",
    "FacetGrid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex',\n",
    "              palette=None, order=None, hue_order=None) \n",
    "\n",
    "FacetGrid.add_legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**登船口有非常大的影响**\n",
    "\n",
    "1. 最底下，登船口为Q的男性1, 2等舱的生存率为0，而女性生存率为100，这不禁让人联想到是不是登记错了。\n",
    "\n",
    "2. 除了最下面一条折线，一等舱的生存率都是最高的\n",
    "\n",
    "3. 其余两幅图，女性的生存率都大于男性，只有第二幅图登船口为C的男性大。\n",
    "\n",
    "4. 处在上方的折线，在二等舱到三等舱时生存率都有急剧的下降；处在下方的折线，2 3等舱基本持平，第二、三幅图甚至3等舱高一点\n",
    "\n",
    "目前我还没有有关登船口的信息，不清楚为何影响这么大\n",
    "\n",
    "**每一个点的竖线代表范围？**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17728ebd160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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   ],
   "source": [
    "sns.barplot(x='Pclass', y='Survived', data=train_df)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很明显看到Pclass对生存率的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x17728c87ef0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass',\n",
    "size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. SibSp and Parch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SibSp: 兄弟、配偶（同辈的）\n",
    "\n",
    "Parch: 子女、父母\n",
    "\n",
    "作者在这里把这两个作为一个特征，表明该乘客在船上是否alone。我觉得SibSp对生存是有帮助的，而Parch可能更需要我们的帮助，所以两个特征不能和到一块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    537\n",
       "1    354\n",
       "Name: not_alone, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [train_df, test_df]\n",
    "for dataset in data:\n",
    "    # 加了两列, relatives和not_alone\n",
    "    dataset['relatives'] = dataset['SibSp'] + dataset['Parch']\n",
    "    dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 1\n",
    "    dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 0\n",
    "    dataset['not_alone'] = dataset['not_alone'].astype(int)\n",
    "\n",
    "train_df['not_alone'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 14 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      " 12  relatives    891 non-null    int64  \n",
      " 13  not_alone    891 non-null    int32  \n",
      "dtypes: float64(2), int32(1), int64(6), object(5)\n",
      "memory usage: 94.1+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   <g id=\"patch_3\">\r\n",
       "    <path d=\"M 43.78125 314.6 \r\n",
       "L 43.78125 7.2 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "   <g id=\"patch_4\">\r\n",
       "    <path d=\"M 43.78125 314.6 \r\n",
       "L 885.18125 314.6 \r\n",
       "\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n",
       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"p69076f7b44\">\r\n",
       "   <rect height=\"307.4\" width=\"841.4\" x=\"43.78125\" y=\"7.2\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 900x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = sns.factorplot('relatives', 'Survived', data=train_df, aspect=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图我们可以发现作者的这次分析没有什么卵用\n",
    "\n",
    "我还是想把两个变量分开分析一下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {
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   ],
   "source": [
    "axes = sns.factorplot('SibSp', 'Survived', data=train_df, aspect=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = sns.factorplot('Parch', 'Survived', data=train_df, aspect=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "\n",
    "drop PassengerId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['PassengerId'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Data\n",
    "\n",
    "处理缺失数据，Cabin(687), Embarked(2), Age(177)\n",
    "\n",
    "### Cabin\n",
    "\n",
    "本来是要丢掉Cabin数据的，但作者发现Cabin编号中包含了乘客的'deck'，决定提出新的特征。我也不知道这deck是不是层的意思，怎么从c123中看出来的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully drop the fature!\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "deck = {\"A\": 1, \"B\": 2, \"C\": 3, \"D\": 4, \"E\": 5, \"F\": 6, \"G\": 7, \"U\": 8}\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Cabin'] = dataset['Cabin'].fillna('U0')\n",
    "    dataset['Deck'] = dataset['Cabin'].map(lambda x: re.compile(\"([a-zA-z]+)\").search(x).group())\n",
    "    dataset['Deck'] = dataset['Deck'].map(deck)\n",
    "    dataset['Deck'] = dataset['Deck'].fillna(0)\n",
    "    dataset['Deck'] = dataset['Deck'].astype(int)\n",
    "\n",
    "# drop the cabin feature\n",
    "train_df = train_df.drop(['Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Cabin'], axis=1)\n",
    "print(\"Successfully drop the fature!\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age\n",
    "\n",
    "生成一个随机矩阵，值为范围在mean +- std间的随机值，然后把随机值赋给缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    mean = train_df['Age'].mean()\n",
    "    std = test_df['Age'].std()\n",
    "    is_null = dataset['Age'].isnull().sum()\n",
    "\n",
    "    rand_age = np.random.randint(mean - std, mean + std, size=is_null)\n",
    "\n",
    "    age_slice = dataset['Age'].copy()\n",
    "    age_slice[np.isnan(age_slice)] = rand_age\n",
    "    dataset['Age'] = age_slice\n",
    "    dataset['Age'] = train_df['Age'].astype(int)\n",
    "\n",
    "train_df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embarked\n",
    "\n",
    "只有两个缺失，直接用出现次数最多的值代替"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     889\n",
       "unique      3\n",
       "top         S\n",
       "freq      644\n",
       "Name: Embarked, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Embarked'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_value = 'S'\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(common_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Survived   891 non-null    int64  \n",
      " 1   Pclass     891 non-null    int64  \n",
      " 2   Name       891 non-null    object \n",
      " 3   Sex        891 non-null    object \n",
      " 4   Age        891 non-null    int32  \n",
      " 5   SibSp      891 non-null    int64  \n",
      " 6   Parch      891 non-null    int64  \n",
      " 7   Ticket     891 non-null    object \n",
      " 8   Fare       891 non-null    float64\n",
      " 9   Embarked   891 non-null    object \n",
      " 10  relatives  891 non-null    int64  \n",
      " 11  not_alone  891 non-null    int32  \n",
      " 12  Deck       891 non-null    int32  \n",
      "dtypes: float64(1), int32(3), int64(5), object(4)\n",
      "memory usage: 80.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare:\n",
    "\n",
    "float型，需要转化成int64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Fare'] = dataset['Fare'].fillna(0)\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name:\n",
    "\n",
    "extract the titles(mr, mrs, master)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "titles = {'Mr': 1, 'Miss': 2, 'Mrs': 3, 'Master': 4, 'Rare': 5}\n",
    "\n",
    "for dataset in data:\n",
    "    # extract titles\n",
    "    dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "    # replace unique titles with a more common title or as rare\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mile', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    # convert titles into numbers\n",
    "    dataset['Title'] = dataset['Title'].map(titles)\n",
    "    # filling NaN with 0, to get safe\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df = train_df.drop(['Name'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Survived   891 non-null    int64  \n",
      " 1   Pclass     891 non-null    int64  \n",
      " 2   Sex        891 non-null    object \n",
      " 3   Age        891 non-null    int32  \n",
      " 4   SibSp      891 non-null    int64  \n",
      " 5   Parch      891 non-null    int64  \n",
      " 6   Ticket     891 non-null    object \n",
      " 7   Fare       891 non-null    int32  \n",
      " 8   Embarked   891 non-null    object \n",
      " 9   relatives  891 non-null    int64  \n",
      " 10  not_alone  891 non-null    int32  \n",
      " 11  Deck       891 non-null    int32  \n",
      " 12  Title      891 non-null    float64\n",
      "dtypes: float64(1), int32(4), int64(5), object(3)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "genders = {'male': 0, 'female': 1}\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(genders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Survived   891 non-null    int64  \n",
      " 1   Pclass     891 non-null    int64  \n",
      " 2   Sex        891 non-null    int64  \n",
      " 3   Age        891 non-null    int32  \n",
      " 4   SibSp      891 non-null    int64  \n",
      " 5   Parch      891 non-null    int64  \n",
      " 6   Ticket     891 non-null    object \n",
      " 7   Fare       891 non-null    int32  \n",
      " 8   Embarked   891 non-null    object \n",
      " 9   relatives  891 non-null    int64  \n",
      " 10  not_alone  891 non-null    int32  \n",
      " 11  Deck       891 non-null    int32  \n",
      " 12  Title      891 non-null    float64\n",
      "dtypes: float64(1), int32(4), int64(6), object(2)\n",
      "memory usage: 76.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      891\n",
       "unique     681\n",
       "top       1601\n",
       "freq         7\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Ticket'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "直接drop掉，681个都不一样分析尼玛"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['Ticket'], axis=1)\n",
    "test_df = test_df.drop(['Ticket'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ports = {'S': 0, 'C': 1, 'Q': 2}\n",
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map(ports)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age:\n",
    "\n",
    "分组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "for dataset in data:\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "    dataset.loc[dataset['Age'] <= 11, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3\n",
    "    dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4\n",
    "    dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5\n",
    "    dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6\n",
    "    dataset.loc[ dataset['Age'] > 66, 'Age'] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    163\n",
       "5    158\n",
       "6    154\n",
       "3    139\n",
       "2    106\n",
       "1     96\n",
       "0     68\n",
       "7      7\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fare:\n",
    "\n",
    "如果均匀cut，fare都会挤到一堆，所以这里用qcut()函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  relatives  \\\n",
       "0         0       3    0    2      1      0     7         0          1   \n",
       "1         1       1    1    5      1      0    71         1          1   \n",
       "2         1       3    1    3      0      0     7         0          0   \n",
       "3         1       1    1    5      1      0    53         0          1   \n",
       "4         0       3    0    5      0      0     8         0          0   \n",
       "5         0       3    0    5      0      0     8         2          0   \n",
       "6         0       1    0    6      0      0    51         0          0   \n",
       "7         0       3    0    0      3      1    21         0          4   \n",
       "8         1       3    1    3      0      2    11         0          2   \n",
       "9         1       2    1    1      1      0    30         1          1   \n",
       "\n",
       "   not_alone  Deck  Title  \n",
       "0          1     8    1.0  \n",
       "1          1     3    3.0  \n",
       "2          0     8    2.0  \n",
       "3          1     3    3.0  \n",
       "4          0     8    1.0  \n",
       "5          0     8    1.0  \n",
       "6          0     5    1.0  \n",
       "7          1     8    4.0  \n",
       "8          1     8    3.0  \n",
       "9          1     8    3.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "\n",
    "for dataset in data:\n",
    "    dataset.loc[dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2\n",
    "    dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare'] <= 99), 'Fare'] = 3\n",
    "    dataset.loc[(dataset['Fare'] > 99) & (dataset['Fare'] <= 250), 'Fare'] = 4\n",
    "    dataset.loc[ dataset['Fare'] > 250, 'Fare'] = 5\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Age times class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df, test_df]\n",
    "for dataset in data:\n",
    "    dataset['Age_Class'] = dataset['Age'] * dataset['Pclass']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Fare per person\n",
    "\n",
    "这个特征我真的没看懂 表现家庭条件？？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>relatives</th>\n",
       "      <th>not_alone</th>\n",
       "      <th>Deck</th>\n",
       "      <th>Title</th>\n",
       "      <th>Age_Class</th>\n",
       "      <th>Fare_Per_Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  relatives  \\\n",
       "0         0       3    0    2      1      0     0         0          1   \n",
       "1         1       1    1    5      1      0     3         1          1   \n",
       "2         1       3    1    3      0      0     0         0          0   \n",
       "3         1       1    1    5      1      0     3         0          1   \n",
       "4         0       3    0    5      0      0     1         0          0   \n",
       "5         0       3    0    5      0      0     1         2          0   \n",
       "6         0       1    0    6      0      0     3         0          0   \n",
       "7         0       3    0    0      3      1     2         0          4   \n",
       "8         1       3    1    3      0      2     1         0          2   \n",
       "9         1       2    1    1      1      0     2         1          1   \n",
       "\n",
       "   not_alone  Deck  Title  Age_Class  Fare_Per_Person  \n",
       "0          1     8    1.0          6                0  \n",
       "1          1     3    3.0          5                1  \n",
       "2          0     8    2.0          9                0  \n",
       "3          1     3    3.0          5                1  \n",
       "4          0     8    1.0         15                1  \n",
       "5          0     8    1.0         15                1  \n",
       "6          0     5    1.0          6                3  \n",
       "7          1     8    4.0          0                0  \n",
       "8          1     8    3.0          9                0  \n",
       "9          1     8    3.0          2                1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in data:\n",
    "    dataset['Fare_Per_Person'] = dataset['Fare'] / (dataset['relatives'] + 1)\n",
    "    dataset['Fare_Per_Person'] = dataset['Fare_Per_Person'].astype(int)\n",
    "\n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building machine learning models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Several models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来训练几个模型并做对比。\n",
    "\n",
    "test数据没有提供标签（为了比赛吧），所以得把model应用在train的数据上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_df.drop('Survived', axis=1)\n",
    "Y_train = train_df['Survived']\n",
    "X_test = test_df.drop('PassengerId', axis=1).copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = linear_model.SGDClassifier(max_iter=5, tol=None)\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "\n",
    "sgd.score(X_train, Y_train)\n",
    "\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "\n",
    "Y_prediction = random_forest.predict(X_test)\n",
    "\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = logreg.predict(X_test)\n",
    "\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K nearest neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaussian naive bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "perceptron = Perceptron(max_iter=5)\n",
    "perceptron.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear support vector machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "decision_tree = DecisionTreeClassifier() \n",
    "decision_tree.fit(X_train, Y_train) \n",
    "\n",
    "Y_pred = decision_tree.predict(X_test) \n",
    "\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the best model\n",
    "\n",
    "有点迷，直接拿训练数据集测试准确率，过拟合咋整？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Score</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92.48</th>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92.48</th>\n",
       "      <td>Decision Tree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86.42</th>\n",
       "      <td>KNN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81.26</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81.26</th>\n",
       "      <td>Logistic Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79.35</th>\n",
       "      <td>Perceptron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77.89</th>\n",
       "      <td>Naive Bayes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67.34</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Model\n",
       "Score                            \n",
       "92.48               Random Forest\n",
       "92.48               Decision Tree\n",
       "86.42                         KNN\n",
       "81.26     Support Vector Machines\n",
       "81.26         Logistic Regression\n",
       "79.35                  Perceptron\n",
       "77.89                 Naive Bayes\n",
       "67.34  Stochastic Gradient Decent"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression',\n",
    "'Random Forest', 'Naive Bayes', 'Perceptron',\n",
    "'Stochastic Gradient Decent',\n",
    "'Decision Tree'],\n",
    "'Score': [acc_linear_svc, acc_knn, acc_log,\n",
    "acc_random_forest, acc_gaussian, acc_perceptron,\n",
    "acc_sgd, acc_decision_tree]})\n",
    "result_df = results.sort_values(by='Score', ascending=False)\n",
    "result_df = result_df.set_index('Score')\n",
    "result_df.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到random forest算法正确率最高\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-Fold cross validation\n",
    "\n",
    "k折交叉验证 毕设的SVM用过，找最佳参数的\n",
    "\n",
    "这里把数据分成10份，每次随机训练9组数据，剩下一份作为验证集，直到每组数据都被但当作验证数据集为止"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [0.74444444 0.80898876 0.73033708 0.83146067 0.85393258 0.86516854\n",
      " 0.80898876 0.7752809  0.85393258 0.84269663]\n",
      "Mean: 0.8115230961298376\n",
      "Standard deviation: 0.04501257225782718\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100)\n",
    "scores = cross_val_score(rf, X_train, Y_train, cv=10, scoring='accuracy')\n",
    "\n",
    "print('Scores:', scores)\n",
    "print('Mean:', scores.mean())\n",
    "print('Standard deviation:', scores.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest\n",
    "\n",
    "## What is random forest\n",
    "\n",
    "\n",
    "## Feature importance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <td>0.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>0.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age_Class</th>\n",
       "      <td>0.097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deck</th>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>0.073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relatives</th>\n",
       "      <td>0.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <td>0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare_Per_Person</th>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_alone</th>\n",
       "      <td>0.011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 importance\n",
       "feature                    \n",
       "Title                 0.190\n",
       "Sex                   0.177\n",
       "Age_Class             0.097\n",
       "Deck                  0.082\n",
       "Age                   0.079\n",
       "Pclass                0.073\n",
       "relatives             0.067\n",
       "Fare                  0.066\n",
       "Embarked              0.058\n",
       "Fare_Per_Person       0.040\n",
       "SibSp                 0.038\n",
       "Parch                 0.020\n",
       "not_alone             0.011"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances = pd.DataFrame({'feature': X_train.columns, 'importance': np.round(random_forest.feature_importances_, 3)})\n",
    "importances = importances.sort_values('importance', ascending=False).set_index('feature')\n",
    "\n",
    "importances.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17725244eb8>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances.plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出parch和not_alone重要性很低，drop掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = train_df.drop('not_alone', axis=1)\n",
    "test_df = test_df.drop('not_alone', axis=1)\n",
    "\n",
    "train_df = train_df.drop('Parch', axis=1)\n",
    "test_df = test_df.drop('Parch', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里用`for dataset in data`这种方式没有用\n",
    "\n",
    "可能迭代的dataset只是个指针，这种操作只让这个临时指针指向了新的对象"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Tranining again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92.48 %\n"
     ]
    }
   ],
   "source": [
    "random_forest = RandomForestClassifier(n_estimators=100, oob_score = True)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_prediction = random_forest.predict(X_test)\n",
    "\n",
    "random_forest.score(X_train, Y_train)\n",
    "\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "print(round(acc_random_forest,2,), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然后用一个叫out of bag samples的方法测试准确性，好像很吊，相当于用和训练集同等大小的测试集测试\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oob score:  81.93 %\n"
     ]
    }
   ],
   "source": [
    "print('oob score: ', round(random_forest.oob_score_, 4) * 100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_params'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-53-690a30d74d4b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params'"
     ]
    }
   ],
   "source": [
    "param_grid = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'min_samples_leaf': [1, 5, 10, 25, 50, 70],\n",
    "    'min_samples_split': [2, 4, 10, 12, 16, 18, 25, 35],\n",
    "    'n_estimators': [100, 400, 700, 1000, 1500]\n",
    "}\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, max_features='auto', \n",
    "oob_score=True, random_state=1, n_jobs=-1)\n",
    "clf = GridSearchCV(estimator=rf, param_grid=param_grid, n_jobs=-1)\n",
    "\n",
    "clf.fit(X_train, Y_train)\n",
    "\n",
    "# clf.best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 10,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test new parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "oob score: 82.72 %\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "random_forest = RandomForestClassifier(criterion = \"gini\",\n",
    "min_samples_leaf = 1,\n",
    "min_samples_split = 10,\n",
    "n_estimators=100,\n",
    "max_features='auto',\n",
    "oob_score=True,\n",
    "random_state=1,\n",
    "n_jobs=-1)\n",
    "\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_prediction = random_forest.predict(X_test)\n",
    "\n",
    "random_forest.score(X_train, Y_train)\n",
    "\n",
    "print(\"oob score:\", round(random_forest.oob_score_, 4)*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "more complicated to evaluate a classification model than a regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[488,  61],\n",
       "       [ 88, 254]], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "predictions = cross_val_predict(random_forest, X_train, Y_train, cv=3)\n",
    "confusion_matrix(Y_train, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一行是预测未存活的乘客：488个预测对了(true nagtives)，61个错了(false positives)\n",
    "\n",
    "第二行是预测存活的乘客：254个对了(true positives)，88个错了(false negtives)\n",
    "\n",
    "可以分别看正反的正确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision and recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.8063492063492064\n",
      "Recall: 0.7426900584795322\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "print('Precision:', precision_score(Y_train, predictions))\n",
    "print('Recall:', recall_score(Y_train, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model predicts 81% of the time, a passengers survival correctly (precision). The recall tells us that it predicted the survival of 73 % of the people who actually survived.\n",
    "\n",
    "Precision：TP / (TP + FP), 正确的正向占预测的正向的比例\n",
    "\n",
    "Recall: Tp / (TP + FN), 正确的正向占实际的正向的比例\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F-Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从precision和recall的定义可以看出，他们两个都，越高，说明模型效果越好。\n",
    "\n",
    "所以有了这个综合它们两者的参数，对他们两个求**调和**平均数\n",
    "\n",
    "低的占比会更大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7732115677321156"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "\n",
    "f1_score(Y_train, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "然而作者又说了，precision和recall就像测不准定理似的，两者不能同时变高。\n",
    "\n",
    "有的时候需要搞得precision, 有的时候需要recall\n",
    "\n",
    "所以需要更精确的测量这两个参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision recall curve\n",
    "\n",
    "对每个分类的数据，如果最后得出的值超过了阈值，就分类到正向(Survived)\n",
    "\n",
    "用threhold和precision和recall画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "   </g>\r\n",
       "  </g>\r\n",
       " </g>\r\n",
       " <defs>\r\n",
       "  <clipPath id=\"p7126c27d00\">\r\n",
       "   <rect height=\"380.52\" width=\"781.2\" x=\"30.103125\" y=\"10.999219\"/>\r\n",
       "  </clipPath>\r\n",
       " </defs>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "y_scores = random_forest.predict_proba(X_train)\n",
    "y_scores = y_scores[:, 1]\n",
    "\n",
    "precision, recall, threshold = precision_recall_curve(Y_train, y_scores)\n",
    "\n",
    "def plot_precision_and_recall(precision, recall, threshold):\n",
    "    plt.plot(threshold, precision[:-1], 'r-', label='precision', linewidth=5)\n",
    "    plt.plot(threshold, recall[:-1], 'b', label='recall', linewidth=5)\n",
    "    plt.xlabel('threshold', fontsize=19)\n",
    "    plt.legend(loc='upper right', fontsize=19)\n",
    "    plt.ylim([0, 1])\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plot_precision_and_recall(precision, recall, threshold)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据这个曲线，我们可以按precision和recall的具体需求，选择合适的threshold\n",
    "\n",
    "另一种横纵坐标分别为recall和precision的画法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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   "source": [
    "def plot_precision_vs_recall(precision, recall):\n",
    "    plt.plot(recall, precision, 'g--', linewidth=2.5)\n",
    "    plt.ylabel('recall', fontsize=19)\n",
    "    plt.xlabel('precision', fontsize=19)\n",
    "    plt.axis([0, 1.5, 0, 1.5])\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plot_precision_vs_recall(precision, recall)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC AUC Curve\n",
    "\n",
    "true positive rate versus false positive rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
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   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(Y_train, y_scores)\n",
    "\n",
    "def plot_roc_curve(false_positive_rate, true_positive_rate, label=None):\n",
    "    plt.plot(false_positive_rate, true_positive_rate, linewidth=2, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'r', linewidth=4)\n",
    "    plt.axis([0, 1, 0, 1])\n",
    "    plt.xlabel('False positive rate (FPR)', fontsize=16)\n",
    "    plt.ylabel('True positive rate (TPR)', fontsize=16)\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "plot_roc_curve(false_positive_rate, true_positive_rate)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "红色的线是完全随机的分类，所以我们模型的线要尽可能原理这种无序的状况\n",
    "\n",
    "看起来还不错"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROC AUC Score\n",
    "\n",
    "It is simply computed by measuring the area under the curve, which is called AUC.\n",
    "\n",
    "所以完全准确的分类，值为1；随机分类，值为0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC-AUC-Score: 0.9441967852235325\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "r_a_score = roc_auc_score(Y_train, y_scores)\n",
    "print('ROC-AUC-Score:', r_a_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结与改进\n",
    "\n",
    "Of course there is still room for improvement, like doing a more extensive feature engineering, by comparing and plotting the features against each other and identifying and removing the noisy features. Another thing that can improve the overall result on the kaggle leaderboard would be a more extensive hyperparameter tuning on several machine learning models. You could also do some ensemble learning."
   ]
  }
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